2021 and Beyond: The Evolving Fraud Landscape

Fraud
Management/mitigation
Abuse
Account Takeover
Cybercrime
New Account Fraud
Kevin Gosschalk -- Arkose Labs; Shreyas Nangalia -- LinkedIn
Jan 28, 2021
Webinars
The COVID-19 pandemic and related lockdowns spurred a massive increase in digital traffic, which also brought with it a rise in fraud as attackers sought to blend in with good users. This webinar discusses ways in which merchants can combat fraud in this continuing "new normal" and how to do so while still providing a positive purchase experience for legitimate customers. Deep dives on three important fraud patterns are also included.

Some content is hidden, to be able to see it login here Login

Blue-tinted background of a man watching a webinar

Host a Webinar with the MRC

Help the MRC community stay current on relevant fraud, payments, and law enforcement topics.
Submit a Request

Publish Your Document with the MRC

Feature your case studies, surveys, and whitepapers in the MRC Resource Center.
Submit Your Document

Related Resources

Mar 08, 2023
Customer Recognition System - A New Tool of Detecting Fraud

Today, link analytics has been widely used across a variety of applications and industries (e.g., telecommunications, social networking, healthcare, finance ) to identify or predict the association of different entities behind the scenes. Companies with multiple product offerings use this technology to learn from their customers’ data to provide better user experiences. 

At Intuit, our customer recognition system (named Core ID, internally)  is focused on finding out if one customer or one family/close cluster uses one or more entities to register many accounts for products, such as QuickBooks Payments and QuickBooks Payroll customers. 

Normally, if the customer is identified with one set of entities, we can use existing solutions for ID-mapping, which rely on “exact” matching among entities to create clusters and graphs. However, this absolute linkage will fail if the customer is associated with multiple entities or changes entities (device IDs, IP address, etc.). 

To solve this problem, we have devised a methodology for recognizing one customer, or one household, from different angles by applying several AI-driven technologies. 

Intuit’s customer recognition system reveals relationships among different entities, serving as a complement to existing linkage-based graph analytics to more quickly identify or predict the association between customer accounts. Understanding these underlying connections more quickly is one strategy for building long-lasting customer relationships.

X
Cookies help us improve your website experience.
By using our website, you agree to our use of cookies.
Confirm